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Reka Bentuk Faktorial Pecahan Bantuan Pengoptimuman×Reka Bentuk Eksperimen×
BidangReka Bentuk EksperimenReka Bentuk Eksperimen
KeluargaProcess / pipelineProcess / pipeline
Tahun asal1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995)1935
PengasasA. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms)Ronald A. Fisher
JenisOptimal experimental design / computer-generated DOEExperimental planning framework
Sumber perintisAtkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. ISBN: 978-0199296606Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗
Aliasoptimal fractional factorial design, algorithmically optimized FFD, computer-aided fractional factorial design, D-optimal fractional factorial designDOE, experimental design, factorial experimentation, planned experimentation
Berkaitan43
RingkasanOptimization-assisted fractional factorial design (OA-FFD) combines classical fractional factorial screening with algorithmic optimality criteria — such as D-, I-, or A-optimality — to construct experiment matrices that maximize statistical efficiency. Instead of relying solely on standard orthogonal-array tables, a computer algorithm selects the best subset of runs from a candidate set, enabling experimenters to handle irregular factor constraints, mixed factor types, and custom run sizes that standard tables cannot accommodate.Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.
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ScholarGateBandingkan kaedah: Optimization-assisted fractional factorial design · Design of experiments. Dicapai 2026-06-19 daripada https://scholargate.app/ms/compare